In hands-free telephony and other distant-talking applications, an acoustic echo cancellation system is typically required, where a short adaptive filter is often used in practice to achieve fast convergence at low computational cost. This may result in late residual echo (LRE) remaining due to under-modeling of the echo path and early residual echo (ERE) due to filter misalignment. Both residual echo components can be suppressed using a postfilter in the subband domain, which requires accurate estimates of the power spectral density (PSD) of the ERE and LRE components. State-of-the-art methods estimate the ERE and LRE PSDs independently of each other, where the ERE PSD is estimated by simply multiplying the loudspeaker PSD with a frequency-dependent scalar and the LRE PSD is estimated using a recursive estimator based on frequency-dependent reverberation scaling and decay parameters. In this paper, we propose to extend the ERE PSD estimator from a scalar to a moving average filter on the loudspeaker PSD. In addition, we propose a signal-based method to jointly estimate all model parameters for the ERE and LRE PSD estimators in online mode, and derive two gradient-descent-based algorithms to simultaneously update the model parameters by minimizing the mean squared log error. The proposed method is compared with state-of-the-art methods in terms of estimation accuracy of the model parameters as well as the residual echo PSDs. Simulation results using both artificially generated as well as measured impulse responses show that the proposed method outperforms state-of-the-art methods for all considered scenarios.

In hands-free telephony and other distant-talk applications, often a short AEC filter is used to achieve fast convergence at low computational cost. As a result, a significant amount of late residual echo (LRE) may remain, especially in highly reverberant environments. This LRE can be suppressed using a postfilter in the subband domain, which requires an estimate of the power spectral density (PSD) of the LRE. To estimate the LRE PSD, an exponentially decaying model with frequency-dependent reverberation scaling and decay parameters has frequently been assumed. State-of-the-art methods estimate both reverberation parameters independently of each other, either in offline or in online mode. In this article, we propose two signal-based methods (i.e. output error and equation error) to jointly estimate both reverberation parameters in online mode. The estimated parameters are then used to generate an estimate for the LRE PSD, which is fed into a postfilter for the purpose of late residual echo suppression. We derive several gradient-descent-based algorithms to simultaneously update both reverberation parameters, minimizing either the mean squared error or the mean squared log error cost function. The proposed methods are compared with state-of-the-art methods in terms of the accuracy of the estimated reverberation parameters and the corresponding LRE PSD estimate. Extensive simulation results using both artificial as well as measured room impulse responses show that the proposed output error method with mean squared log error minimization outperforms state-of-the-art methods in all considered scenarios.

In cars with integrated distributed microphone systems usually each speaker has a dedicated microphone. An often required broadband speaker activity detection can be performed by simply evaluating the power ratios among the microphones but transient interferers like indicator noise, outside crossing cars or speech from interfering speakers may be wrongly assigned to one speaker's activity. In this contribution a new method is presented that exploits patterns based on the characteristics of signal power inverted subbands at which compared to the closest microphone higher energy occurs in a distant one. By determination of a distance measure the currently observed pattern of these power inverted subbands is compared to online learned speaker position dependent reference patterns. During noise only periods as well as during transient interfering signals the patterns do not match the reference and false speaker activity detections can be reduced.

Compact microphone arrays allow for directional filtering with a minimum of installation space. They are therefore particularly suitable for automotive applications. Typically, compact arrays are realized as differential arrays or filter-and-sum beamformers which both show limited performance in terms of directivity. In this contribution we present a novel system for directional filtering for compact arrays. This system consists of two closely spaced microphones and incorporates an adaptive beamformer as well as a spatial post-filter which is designed to suppress non-stationary noise.